QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning
QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning
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DOI：
10.1109/tvt.2022.3152146
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发表时间：
2022-05-01
影响因子：
6.8
通讯作者：
Zahemszky, Andras
中科院分区：
文献类型：
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作者：
Burhanuddin, Liyana Adilla Binti;Liu, Xiaonan;Zahemszky, Andras
在偏远地区与野火作战时，救援队面临的一个挑战是缺乏信息，例如火灾区域的大小和图像。因此，来自无人机（UAV）的实时流媒体，捕捉动态火灾区域的视频，对于任何地点的消防指挥官都至关重要，可以快速响应地监控火灾情况。5G网络是支持此类场景的有前途的无线技术。在本文中，我们考虑了一个无人机到无人机（U2 U）的通信场景，在高空的无人机作为一个移动的基站（UAV-BS）流视频从其他飞行的无人机用户（UAV-UE）通过上行链路。由于UAV-BS和UAV-UE的移动性，重要的是实时确定UAV-BS和UAV-UE的最佳移动和传输功率，以便在平滑和低延迟的情况下最大化视频传输的数据速率，同时根据火灾区域和无线信道条件的动态来减轻干扰。在本文中，我们共同设计的视频分辨率，运动，和UAV-BS和UAV-UE的功率控制，以最大限度地提高实时视频流的体验质量（QoE）。我们应用深度Q网络（DQN）和演员评论（AC）来最大化从所有UAV-UE到单个UAV-BS的视频传输的QoE，以了解动态火灾区域和通信环境。仿真结果表明，我们提出的算法的有效性，在QoE，延迟和视频平滑度相比，贪婪算法。
A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in real-time, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. We applied the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAV-BS to learn the dynamic fire areas and communication environment. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness compared to the Greedy algorithm.